Software for Agents

Build production-ready infrastructure software that makes AI agents reliable, observable, and safe enough to operate autonomously in high-stakes production environments. Core vision: Create the foundational software layer that AI agents need to work reliably at scale (covering agent orchestration, persistent memory across sessions, tool and API integration, error recovery, observability, safety guardrails, and multi-agent coordination), solving the infrastructure problems that prevent AI agents from being trusted with important autonomous work today. The system must support the full agent infrastructure stack: 1. Reliable agent execution with state persistence and recovery 2. Tool and API integration with retry logic and error handling 3. Memory systems that persist context across sessions 4. Multi-agent coordination and communication protocols 5. Observability: trace every decision, action, and outcome 6. Safety: guardrails that prevent agents from taking dangerous actions Core capabilities: Agent execution runtime: - Durable task execution that survives process restarts and network failures - Checkpointing: save agent state at every step for replay and recovery - Timeout and retry policies configurable per task type - Parallel subtask execution with dependency graph management - Resource usage limits (token budget, API call rate limits, time limits) - Graceful degradation when tools or APIs are unavailable Persistent memory architecture: - Episodic memory: recall what the agent did in past sessions - Semantic memory: vector-indexed knowledge base the agent can query - Working memory: structured context for the current task - Memory compression: summarize old context without losing key facts - Cross-agent memory sharing with access controls - Memory audit trail for debugging agent decisions Tool and API integration layer: - Standardized tool definition format with input/output schemas - Automatic retry with exponential backoff and circuit breakers - Tool capability versioning and backwards compatibility - Sandboxed code execution environment for agent-written code - Browser automation with session persistence - File system access with permissions enforcement Multi-agent coordination: - Agent-to-agent communication protocol with message queuing - Role assignment and task delegation between specialized agents - Consensus mechanisms for decisions requiring multiple agent agreement - Conflict detection when agents take contradictory actions - Orchestrator-worker patterns with supervisor oversight - Shared context injection for agents working on related tasks Observability and debugging: - Full trace of every agent decision with reasoning captured - Tool call logs with inputs, outputs, latencies, and costs - Memory read/write audit trail - Anomaly detection for unexpected agent behavior patterns - Cost tracking per task, session, and customer - Replay any past agent session step-by-step for debugging Safety and guardrails: - Action allowlist/blocklist configurable by deployment context - Human-in-the-loop approval gates for high-risk actions - Sensitive data detection before external API calls - Rate limiting to prevent runaway agents - Irreversibility detection: warn before agents take hard-to-undo actions - Emergency stop mechanisms at task and agent-fleet level Build a working demo covering: 1. Start a multi-step agent task and kill the process mid-execution; show recovery 2. View full trace of an agent's reasoning, tool calls, and decisions 3. Trigger a safety guardrail and show the human approval gate 4. Run two coordinating agents and see their message exchange 5. Query agent memory from a previous session in a new task